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Record W1980908219 · doi:10.3390/computation1010016

Structural Features That Stabilize ZnO Clusters: An Electronic Structure Approach

2013· article· en· W1980908219 on OpenAlexafffund
Csaba E. Szakacs, E. S., Kristin M. Poduska

Bibliographic record

VenueComputation · 2013
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCompute Canada
KeywordsWurtzite crystal structureDensity functional theoryFormalism (music)Materials scienceBand gapElectronic structurePeriodic boundary conditionsNanocrystalStructural stabilityKinetic energyChemical physicsNanotechnologyCondensed matter physicsMolecular physicsBoundary value problemComputational chemistryOptoelectronicsChemistryZincPhysicsClassical mechanicsStructural engineeringQuantum mechanics

Abstract

fetched live from OpenAlex

We show that a simple approach to building small computationally inexpensive clusters offers insights on specific structural motifs that stabilize the electronic structure of ZnO. All-electron calculations on ZniOi needle (i = 6, 9, 12, 15, and 18) and plate (i = 9 and 18) clusters within the density functional theory (DFT) formalism show a higher stability for ZnO needles that increases with length. Puckering of the rings to achieve a more wurtzite-like structure destabilizes the needles, although this destabilization is reduced by going to infinite needles (calculated using periodic boundary conditions). Calculations of density of states (DOS) curves and band gaps for finite clusters and infinite needles highlight opportunities for band-gap tuning through kinetic control of nanocrystal growth.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.246
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2013
Admission routes2
Has abstractyes

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